International Journal of Applied Science - Research and Review Open Access

Optimizing 3D Path Planning for Unmanned Ground Vehicles using Improved DDQN

Alexandra Piastra^* Department of Applied Science, University of Rijeka, Croatia
^*Correspondence: Alexandra Piastra, Department of Applied Science, University of Rijeka, Croatia, Email:

Received: 29-May-2024, Manuscript No. IPIAS-24-20927; Editor assigned: 31-May-2024, Pre QC No. IPIAS-24-20927 (PQ); Reviewed: 14-Jun-2024, QC No. IPIAS-24-20927; Revised: 19-Jun-2024, Manuscript No. IPIAS-24-20927 (R); Published: 26-Jun-2024, DOI: 10.36648/2394-9988-11.3.29

Introduction

The development of Unmanned Ground Vehicles (UGVs) has advanced rapidly, driven by applications in military operations, search and rescue, agriculture, and industrial automation. A critical component of UGV functionality is 3D path planning, which involves determining the optimal route for a vehicle to navigate through complex environments. An effective path planning algorithm must account for various obstacles, terrain variations, and dynamic conditions. One of the most promising approaches for enhancing 3D path planning is the use of improved Double Deep Q-Networks (DDQN), a reinforcement learning technique that offers significant improvements in decision-making and navigation accuracy. Path planning in three-dimensional spaces poses unique challenges compared to two-dimensional navigation. UGVs must navigate not only along the ground plane but also account for vertical obstacles such as slopes, hills, and other terrain features. Traditional path planning algorithms, such as A* or Dijkstra’s algorithm, may struggle with the computational complexity and adaptability required for 3D environments. Reinforcement learning (RL), particularly the DDQN approach, provides a robust framework for tackling these challenges by enabling UGVs to learn and adapt to their environments through iterative interactions.

Description

Double Deep Q-Networks (DDQN) is advancement over the traditional Deep Q-Networks (DQN) algorithm. While DQNs have demonstrated success in various applications, they often suffer from overestimation bias, where the algorithm incorrectly estimates the value of certain actions, leading to suboptimal decisions. DDQN addresses this issue by decoupling the action selection and evaluation processes. This separation reduces overestimation and improves the stability and performance of the learning process, making it well-suited for complex 3D path planning tasks. In the context of UGVs, the improved DDQN algorithm can be employed to enhance navigation through a multi-step process. Initially, the UGV’s environment is modeled as a 3D grid or voxel space, where each cell represents a possible position the vehicle can occupy. The UGV is equipped with sensors to perceive its surroundings, providing real-time data about obstacles and terrain features. This sensor data is used to update the state of the environment dynamically, ensuring that the path planning algorithm can respond to changes in real time. The DDQN algorithm then begins its training phase, where the UGV explores the environment and learns to navigate from a starting point to a destination. During this exploration, the UGV takes actions (movements in the 3D space) and receives rewards based on the success of these actions. One of the key advantages of the improved DDQN approach is its ability to handle the exploration-exploitation trade-off effectively. Exploration involves trying new actions to discover potentially better paths, while exploitation focuses on using known actions that yield high rewards. The improved DDQN maintains a balance between these strategies, ensuring that the UGV can explore new paths while leveraging its learned knowledge to make informed decisions.The benefits of using an improved DDQN for 3D path planning in UGVs are numerous. Firstly, the algorithm’s ability to learn from interactions with the environment enables it to adapt to dynamic conditions and unforeseen obstacles, enhancing the UGV’s autonomy and reliability. Secondly, the reduced overestimation bias of DDQN ensures more accurate decision-making, leading to safer and more efficient navigation.

Conclusion

In conclusion, the use of improved DDQN for 3D path planning represents a significant advancement in the field of unmanned ground vehicles. By leveraging the strengths of reinforcement learning and addressing the limitations of traditional algorithms, improved DDQN offers a robust solution for navigating complex 3D environments. As technology continues to evolve, the integration of these advanced algorithms will further enhance the capabilities and applications of UGVs, driving innovation across multiple industries.